Constructing internationally comparable real income aggregates by combining sparse benchmark data with annual national accounts data: A state-space approach

The importance of availability of comparable real income aggregates and their components to applied economic research is highlighted by the popularity of the Penn World Tables. Any methodology designed to achieve such a task requires the combination of data from several sources. The first is purchasing power parities (PPP) data available from the International Comparisons Project roughly every five years since the 1970s. The second is national level data on a range of variables that explain the behaviour of the ratio of PPP to market exchange rates. The final source of data is the national accounts publications of different countries which include estimates of gross domestic product and various price deflators. In this paper we present a method to construct a consistent panel of comparable real incomes by specifying the problem in state-space form. We present our completed work as well as briefly indicate our work in progress.

Urban governance under neoliberalism:New Labour and the restructuring of state-space

In the UK there has been a proliferation of agencies at differing regulatory scales as part of the rescaling and restructuring of the state by New Labour, following the neoliberal policies of previous Conservative governments. This raises questions concerning the extent to which New Labour's urban state restructuring is embedded within neoliberalism, and the local tensions and contradictions arising from emergent New Labour urban state restructuring. This paper examines these questions through the analysis of key policy features of New Labour, and the in-depth exploration of two programmes that are reshaping urban governance arrangements, namely Local Strategic Partnerships (LSPs) and New Deal for Communities (NDC) programmes. We conclude that New Labour's restructuring is best understood in terms of the extended reproduction (roll-out) of neoliberalism. While these “new institutional fixes” are only weakly established and exhibit internal contradictions and tensions, these have not led to a broader contestation of neoliberalism.

Limit Theorems for Non-Critical Branching Processes with Continuous State Space

2000 Mathematics Subject Classification: Primary 60J80, Secondary 60G99.

A Periodic State Space Model to Monthly Long-term Temperature Data

This work presents a periodic state space model to model monthly temperature data. Additionally, some issues are discussed, as the parameter estimation or the Kalman filter recursions adapted to a periodic model. This framework is applied to monthly long-term temperature time series of Lisbon.

At surface behaviour data of juvenile southern elephant seals from Marion Island between 2001 and 2006 with links to datasets

Marine mammals forage in dynamic environments characterized by variables that are continuously changing in relation to large-scale oceanographic processes. In the present study, behavioural states of satellite-tagged juvenile southern elephant seals (n = 16) from Marion Island were assessed for each reliable location, using variation in turning angle and speed in a state-space modelling framework. A mixed modelling approach was used to analyse the behavioural response of juvenile southern elephant seals to sea-surface temperature and proximity to frontal and bathymetric features. The findings emphasised the importance of frontal features as potentially rewarding areas for foraging juvenile southern elephant seals and provided further evidence of the importance of the area west of Marion Island for higher trophic-level predators. The importance of bathymetric features during the transit phase of juvenile southern elephant seal migrations indicates the use of these features as possible navigational cues.

A Krylov-based finite state projection algorithm for solving the chemical master equation arising in the discrete modelling of biological systems

Biochemical reactions underlying genetic regulation are often modelled as a continuous-time, discrete-state, Markov process, and the evolution of the associated probability density is described by the so-called chemical master equation (CME). However the CME is typically difficult to solve, since the state-space involved can be very large or even countably infinite. Recently a finite state projection method (FSP) that truncates the state-space was suggested and shown to be effective in an example of a model of the Pap-pili epigenetic switch. However in this example, both the model and the final time at which the solution was computed, were relatively small. Presented here is a Krylov FSP algorithm based on a combination of state-space truncation and inexact matrix-vector product routines. This allows larger-scale models to be studied and solutions for larger final times to be computed in a realistic execution time. Additionally the new method computes the solution at intermediate times at virtually no extra cost, since it is derived from Krylov-type methods for computing matrix exponentials. For the purpose of comparison the new algorithm is applied to the model of the Pap-pili epigenetic switch, where the original FSP was first demonstrated. Also the method is applied to a more sophisticated model of regulated transcription. Numerical results indicate that the new approach is significantly faster and extendable to larger biological models.

Stochastic modelling of T cell homeostasis for two competing clonotypes via the master equation

Stochastic models for competing clonotypes of T cells by multivariate, continuous-time, discrete state, Markov processes have been proposed in the literature by Stirk, Molina-París and van den Berg (2008). A stochastic modelling framework is important because of rare events associated with small populations of some critical cell types. Usually, computational methods for these problems employ a trajectory-based approach, based on Monte Carlo simulation. This is partly because the complementary, probability density function (PDF) approaches can be expensive but here we describe some efficient PDF approaches by directly solving the governing equations, known as the Master Equation. These computations are made very efficient through an approximation of the state space by the Finite State Projection and through the use of Krylov subspace methods when evolving the matrix exponential. These computational methods allow us to explore the evolution of the PDFs associated with these stochastic models, and bimodal distributions arise in some parameter regimes. Time-dependent propensities naturally arise in immunological processes due to, for example, age-dependent effects. Incorporating time-dependent propensities into the framework of the Master Equation significantly complicates the corresponding computational methods but here we describe an efficient approach via Magnus formulas. Although this contribution focuses on the example of competing clonotypes, the general principles are relevant to multivariate Markov processes and provide fundamental techniques for computational immunology.

Conceptual and statistical modelling of environmental effects in population dynamics

Population dynamics are generally viewed as the result of intrinsic (purely density dependent) and extrinsic (environmental) processes. Both components, and potential interactions between those two, have to be modelled in order to understand and predict dynamics of natural populations; a topic that is of great importance in population management and conservation. This thesis focuses on modelling environmental effects in population dynamics and how effects of potentially relevant environmental variables can be statistically identified and quantified from time series data. Chapter I presents some useful models of multiplicative environmental effects for unstructured density dependent populations. The presented models can be written as standard multiple regression models that are easy to fit to data. Chapters II IV constitute empirical studies that statistically model environmental effects on population dynamics of several migratory bird species with different life history characteristics and migration strategies. In Chapter II, spruce cone crops are found to have a strong positive effect on the population growth of the great spotted woodpecker (Dendrocopos major), while cone crops of pine another important food resource for the species do not effectively explain population growth. The study compares rate- and ratio-dependent effects of cone availability, using state-space models that distinguish between process and observation error in the time series data. Chapter III shows how drought, in combination with settling behaviour during migration, produces asymmetric spatially synchronous patterns of population dynamics in North American ducks (genus Anas). Chapter IV investigates the dynamics of a Finnish population of skylark (Alauda arvensis), and point out effects of rainfall and habitat quality on population growth. Because the skylark time series and some of the environmental variables included show strong positive autocorrelation, the statistical significances are calculated using a Monte Carlo method, where random autocorrelated time series are generated. Chapter V is a simulation-based study, showing that ignoring observation error in analyses of population time series data can bias the estimated effects and measures of uncertainty, if the environmental variables are autocorrelated. It is concluded that the use of state-space models is an effective way to reach more accurate results. In summary, there are several biological assumptions and methodological issues that can affect the inferential outcome when estimating environmental effects from time series data, and that therefore need special attention. The functional form of the environmental effects and potential interactions between environment and population density are important to deal with. Other issues that should be considered are assumptions about density dependent regulation, modelling potential observation error, and when needed, accounting for spatial and/or temporal autocorrelation.

Modelling and control of nonlinear systems using Gaussian processes with partial model information

Gaussian processes are gaining increasing popularity among the control community, in particular for the modelling of discrete time state space systems. However, it has not been clear how to incorporate model information, in the form of known state relationships, when using a Gaussian process as a predictive model. An obvious example of known prior information is position and velocity related states. Incorporation of such information would be beneficial both computationally and for faster dynamics learning. This paper introduces a method of achieving this, yielding faster dynamics learning and a reduction in computational effort from O(Dn2) to O((D - F)n2) in the prediction stage for a system with D states, F known state relationships and n observations. The effectiveness of the method is demonstrated through its inclusion in the PILCO learning algorithm with application to the swing-up and balance of a torque-limited pendulum and the balancing of a robotic unicycle in simulation. © 2012 IEEE.

The Type IIb SN 2011dh: Two years of observations and modelling of the lightcurves

We present optical and near-infrared (NIR) photometry and spectroscopy as well as modelling of the lightcurves of the Type IIb supernova (SN) 2011dh. Our extensive dataset, for which we present the observations obtained after day 100, spans two years, and complemented with Spitzer mid-infrared (MIR) data, we use it to build an optical-to-MIR bolometric lightcurve between days 3 and 732. To model the bolometric lightcurve before day 400 we use a grid of hydrodynamical SN models, which allows us to determine the errors in the derived quantities, and a bolometric correction determined with steady-state non-local thermodynamic equilibrium (NLTE) modelling. Using this method we find a helium core mass of 3.1^+0.7<inf>-0.4</inf> M<inf>⊙</inf> for SN 2011dh, consistent within error bars with previous results obtained using the bolometric lightcurve before day 80. We compute bolometric and broad-band lightcurves between days 100 and 500 from spectral steady-state NLTE models, presented and discussed in a companion paper. The preferred 12 M<inf>⊙</inf> (initial mass) model, previously found to agree well with the observed spectra, shows a good overall agreement with the observed lightcurves, although some discrepancies exist. Time-dependent NLTE modelling shows that after day ∼600 a steady-state assumption is no longer valid. The radioactive energy deposition in this phase is likely dominated by the positrons emitted in the decay of ⁵⁶Co, but seems insufficient to reproduce the lightcurves, and what energy source is dominating the emitted flux is unclear. We find an excess in the K and the MIR bands developing between days 100 and 250, during which an increase in the optical decline rate is also observed. A local origin of the excess is suggested by the depth of the He I 20 581 Å absorption. Steady-state NLTE models with a modest dust opacity in the core (τ = 0.44), turned on during this period, reproduce the observed behaviour, but an additional excess in the Spitzer 4.5 μm band remains. Carbon-monoxide (CO) first-overtone band emission is detected at day 206, and possibly at day 89, and assuming the additional excess to bedominated by CO fundamental band emission, we find fundamental to first-overtone band ratios considerably higher than observed in SN 1987A. The profiles of the [O i] 6300 Å and Mg i] 4571 Å lines show a remarkable similarity, suggesting that these lines originate from a common nuclear burning zone (O/Ne/Mg), and using small scale fluctuations in the line profiles we estimate a filling factor of ≲ 0.07 for the emitting material. This paper concludes our extensive observational and modelling work on SN 2011dh. The results from hydrodynamical modelling, steady-state NLTE modelling, and stellar evolutionary progenitor analysis are all consistent, and suggest an initial mass of ∼12 M<inf>⊙</inf> for the progenitor.

Neurofuzzy control using Kalman filtering state feedback with coloured noise for unknown non-linear processes

This paper presents a controller design scheme for a priori unknown non-linear dynamical processes that are identified via an operating point neurofuzzy system from process data. Based on a neurofuzzy design and model construction algorithm (NeuDec) for a non-linear dynamical process, a neurofuzzy state-space model of controllable form is initially constructed. The control scheme based on closed-loop pole assignment is then utilized to ensure the time invariance and linearization of the state equations so that the system stability can be guaranteed under some mild assumptions, even in the presence of modelling error. The proposed approach requires a known state vector for the application of pole assignment state feedback. For this purpose, a generalized Kalman filtering algorithm with coloured noise is developed on the basis of the neurofuzzy state-space model to obtain an optimal state vector estimation. The derived controller is applied in typical output tracking problems by minimizing the tracking error. Simulation examples are included to demonstrate the operation and effectiveness of the new approach.